Big-data driven intelligent fault diagnosis and prognosis for mechanical systems / Yaguo Lei, Naipeng Li, Xiang Li.
Lei, Yaguo, author.; Li, Naipeng, author.; Li, Xiang, author.
2023
TA169.6
Available Online

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Title Big-data driven intelligent fault diagnosis and prognosis for mechanical systems / Yaguo Lei, Naipeng Li, Xiang Li.
Author Lei, Yaguo, author.
ISBN 9789811691317 (electronic bk.)
9811691312 (electronic bk.)
9789811691300
9811691304
DOI https://doi.org/10.1007/978-981-16-9131-7
Published Singapore : Springer ; [Xi'an] : Xi'an Jiaotong University Press, [2023]
Copyright ©2023
Language English
Description 1 online resource (xiii, 281 pages) : illustrations (chiefly color)
Other Standard Identifiers 10.1007/978-981-16-9131-7 doi
Call Number TA169.6
Dewey Decimal Classification 620/.004
Summary This book presents systematic overviews and bright insights into big data-driven intelligent fault diagnosis and prognosis for mechanical systems. The recent research results on deep transfer learning-based fault diagnosis, data-model fusion remaining useful life (RUL) prediction, etc., are focused on in the book. The contents are valuable and interesting to attract academic researchers, practitioners, and students in the field of prognostics and health management (PHM). Essential guidelines are provided for readers to understand, explore, and implement the presented methodologies, which promote further development of PHM in the big data era. Features: Addresses the critical challenges in the field of PHM at present Presents both fundamental and cutting-edge research theories on intelligent fault diagnosis and prognosis Provides abundant experimental validations and engineering cases of the presented methodologies.
Bibliography, etc. Note Includes bibliographical references.
Access Note Access limited to authorized users.
Source of Description Online resource; title from PDF title page (SpringerLink, viewed October 31, 2022).
Added Author Li, Naipeng, author.
Li, Xiang, author.
Available in Other Form Print version: 9789811691300
Linked Resources Online Access
Record Appears in Online Resources > Ebooks
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Intro
Preface
Contents
About the Authors
1 Introduction and Background
1.1 Introduction
1.1.1 AI Technologies for Data Processing
1.1.2 Big Data-Driven Intelligent Predictive Maintenance
1.1.3 Big Data Analytics Platform Practices
1.2 Overview of Big Data-Driven PHM
1.2.1 Data Acquisition
1.2.2 Data Processing
1.2.3 Diagnosis
1.2.4 Prognosis
1.2.5 Maintenance
1.3 Preface to Book Chapters
References
2 Conventional Intelligent Fault Diagnosis
2.1 Introduction
2.2 Typical Neural Network-Based Methods

2.2.1 Introduction to Neural Networks
2.2.2 Intelligent Diagnosis Using Radial Basis Function Network
2.2.3 Intelligent Diagnosis Using Wavelet Neural Network
2.2.4 Epilog
2.3 Statistical Learning-Based Methods
2.3.1 Introduction to Statistical Learning
2.3.2 Intelligent Diagnosis Using Support Vector Machine
2.3.3 Intelligent Diagnosis Using Relevant Vector Machine
2.3.4 Epilog
2.4 Conclusions
References
3 Hybrid Intelligent Fault Diagnosis
3.1 Introduction
3.2 Multiple WKNN Fault Diagnosis
3.2.1 Motivation

3.2.2 Diagnosis Model Based on Combination of Multiple WKNN
3.2.3 Intelligent Diagnosis Case Study of Rolling Element Bearings
3.2.4 Epilog
3.3 Multiple ANFIS Hybrid Intelligent Fault Diagnosis
3.3.1 Motivation
3.3.2 Multiple ANFIS Combination with GA
3.3.3 Fault Diagnosis Method Based on Multiple ANFIS Combination
3.3.4 Intelligent Diagnosis Case of Rolling Element Bearings
3.3.5 Epilog
3.4 A Multidimensional Hybrid Intelligent Method
3.4.1 Motivation
3.4.2 Multiple Classifier Combination
3.4.3 Diagnosis Method Based on Multiple Classifier Combination

3.4.4 Intelligent Diagnosis Case of Gearboxes
3.4.5 Epilog
3.5 Conclusions
References
4 Deep Transfer Learning-Based Intelligent Fault Diagnosis
4.1 Introduction
4.2 Deep Belief Network for Few-Shot Fault Diagnosis
4.2.1 Motivation
4.2.2 Deep Belief Network-Based Diagnosis Model with Continual Learning
4.2.3 Few-Shot Fault Diagnosis Case of Industrial Robots
4.2.4 Epilog
4.3 Multi-Layer Adaptation Network for Fault Diagnosis with Unlabeled Data
4.3.1 Motivation
4.3.2 Multi-Layer Adaptation Network-Based Diagnosis Model

4.3.3 Fault Diagnosis Case of Locomotive Bearings with Unlabeled Data
4.3.4 Epilog
4.4 Deep Partial Adaptation Network for Domain-Asymmetric Fault Diagnosis
4.4.1 Motivation
4.4.2 Deep Partial Transfer Learning Net-Based Diagnosis Model
4.4.3 Partial Transfer Diagnosis of Gearboxes with Domain Asymmetry
4.4.4 Epilog
4.5 Instance-Level Weighted Adversarial Learning for Open-Set Fault Diagnosis
4.5.1 Motivation
4.5.2 Instance-Level Weighted Adversarial Learning-Based Diagnosis Model
4.5.3 Fault Diagnosis Case of Rolling Bearing Datasets
4.5.4 Epilog

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